1. Field of the Invention
Embodiments of the present invention generally relate to techniques for processing seismic data and, more particularly, to removing noise and separating the wavefields into up- and down-going elements from geophone and hydrophone gathers.
2. Description of the Related Art
In the oil and gas industry, seismic surveys are one of the most important techniques for discovering the presence of subterranean hydrocarbon deposits. If the data is properly processed and interpreted, a seismic survey can provide geologists with a two-dimensional (2-D) or three-dimensional (3-D) representation of subsurface lithologic formations and other features, so that they may better identify those formations likely to contain oil and/or gas. Having an accurate representation of an area's subsurface lithologic formations can increase the odds of hitting an economically recoverable reservoir when drilling and decrease the odds of wasting money and effort on a nonproductive well.
A seismic survey is conducted by deploying an array of energy sources and an array of receivers in an area of interest. Typically, vibrator trucks are used as sources for land surveys, and air guns are used for marine surveys. The sources are discharged in a predetermined sequence, sending a down-going seismic wavefield or signal into the earth that is partially reflected by subsurface seismic reflectors (i.e., interfaces between subsurface lithologic or fluid units having different elastic properties). The reflected or up-going wavefield or signals (known as “seismic reflections”) are then detected and converted to electrical signals by the array of receivers located at or near the surface of the earth, at or near the water surface, or at or near the seafloor.
Each receiver records the amplitude of the incoming signals over time at the receiver's particular location, thereby generating a seismic survey of the subsurface. The seismic energy recorded by each seismic receiver for each source activation during data acquisition is generally referred to as a “trace.” The seismic receivers utilized in such operations typically include pressure sensors, such as hydrophones, and velocity sensors, such as single or multi-component geophones. Since the physical location of the sources and receivers is known, the time it takes for a reflection wave to travel from a source to a sensor is directly related to the depth of the formation that caused the reflection. Thus, the recorded signals, or seismic energy data, from the array of receivers can be analyzed to yield valuable information about the depth and arrangement of the subsurface formations, some of which hopefully contain oil or gas accumulations.
This analysis typically begins by organizing the data from the array of receivers into common geometry gathers, where data from a number of receivers that share a common geometry are analyzed together. A gather will provide information about a particular location or profile in the area being surveyed. Ultimately, the data will be organized into many different gathers and processed before the analysis is completed in an effort to map the entire survey area. The types of gathers typically employed include common midpoint (i.e., the receivers and their respective sources share a common midpoint), common source (i.e., the receivers share a common source), common offset (i.e., the receivers and their respective sources have the same separation or “offset”), and common receiver (i.e., a number of sources share a common receiver).
The data in a gather is typically recorded or first assembled in the time-offset domain. That is, the seismic traces recorded in the gather are assembled or displayed together as a function of offset (i.e., the distance of the receiver from a reference point) and of time. The time required for a given signal to reach and be detected by successive receivers is a function of its velocity and the distance traveled. Those functions are referred to as kinematic travel time trajectories. Thus, at least in theory, when the gathered data is displayed in the time-offset domain (the T-X domain), the amplitude peaks corresponding to reflection signals detected at the receivers should align into patterns that mirror the kinematic travel time trajectories. It is from those trajectories that one ultimately may determine an estimate of the depths at which formations exist.
A number of factors, however, make the practice of seismology and, especially, the interpretation of seismic data much more complicated than its basic principles. Primarily, the up-going reflected signals that indicate the presence of subsurface lithologic formations are typically inundated with various types of noise. The most meaningful signals are the so-called primary reflection signals, those signals that travel down to the reflective surface and then back up to a receiver. When a source is discharged, however, a portion of the signal travels directly to receivers without reflecting off of any subsurface features. In addition, a signal may bounce off of a subsurface feature, bounce off the surface, and then bounce off the same or another subsurface feature, one or more times, creating so-called multiple reflection signals. Other portions of the detected signal may be noise from ground roll, refractions, and unresolvable scattered events. Some noise, both random and coherent, may be generated by natural and man-made events outside the control of the survey, such as wind noise.
All of this noise is detected along with the reflection signals that indicate subsurface features. Thus, the noise and reflection signals tend to overlap when the survey data is displayed in T-X space. The overlap can mask primary reflection signals, the so-called seismic events, and make it difficult or impossible to identify patterns in the display upon which inferences about subsurface geological strata may be drawn. Accordingly, various mathematical methods have been developed to process seismic data in such a way that noise is separated from primary reflection signals.
Many such methods seek to achieve a separation of signal and noise by transforming the data from the T-X domain to other domains, such as the frequency-wavenumber (F-K) or the time-slowness (tau-P) domains, where there is less overlap between the signal and noise data. Once the data is transformed, various mathematical filters are employed to the transformed data to eliminate as much of the noise as possible in an effort to enhance the primary reflection signals. The data is then inverse transformed back into the T-X domain for interpretation or further processing. For example, so-called Radon filters are commonly used to attenuate or remove multiple reflection signals. Such methods rely on Radon transformation equations to transform data from the T-X domain to the tau-P domain where it can be filtered. More specifically, the T-X data is transformed along kinematic travel time trajectories having constant velocities and slownesses, where slowness p is defined as the reciprocal of velocity (p=1/v).
As another example of addressing noise, the combination of dual sensor (hydrophone and vertical geophone) data has long been used as a technique for attenuating ghost reflections from the air water interface. The fundamental concept is that up-going and down-going waves are measured differently by a velocity sensor while direction of progression of the wave has no polarity significance to the hydrophone. At its simplest, dual sensor processing for ghost elimination consists of simply summing recordings made with co-located hydrophones and geophones placed on the sea floor. It has been shown that, for a vertical wave path, a scalar can be applied to one of the two sensors to account for bottom reflectivity and suppress water layer reverberations (see Barr, F. J., and J. I. Sanders, “Attenuation of water-column reverberations using pressure and velocity detectors in a water-bottom cable,” 59th Annual International Meeting, SEG, Expanded Abstracts, pp. 653-656, 1989). Additional corrections are required when the geophone is not firmly coupled with the ocean bottom or there is significant reflectivity in the earth directly and immediately below the position of the sensor. Also, the geophone provides attenuated amplitude sensitivity to waves arriving at the sensor package in a direction other than its presumably vertical orientation while the hydrophone shows indifference to angle of arrival.
Perhaps the largest single obstacle to overcome in wavefield separation of P-Z data is the presence of noise energy (non p-wave specific) on the geophone that is not present on the hydrophone. This noise is observed to some degree on bottom referenced dual sensor data worldwide. It appears to be more severe when near bottom characteristics are more complex or varied. In general, the noise manifests as coherent energy on common receiver gathers with moveout characteristics of converted wave velocity, and with relatively random phase and amplitude on shot gathers. This energy has often been associated with acquisition integrity related issues, such as coupling and phone orientation.
Only recently, alternative explanations have been developed that describe a mechanism that will produce exactly the same type of energy (see Paffenholz, J., P. Docherty, R. Shurtleff, and D. Hays, “Shear wave noise on OBS vz data—Part II: Elastic modeling of scatterers in the seabed,” 68th Conference and Exhibition, EAGE, Expanded Abstracts, B046/B047, 2006). This description explains a physical phenomenon that produces the described characteristics: high amplitude excitation of the particle motion sensed by vertical geophone with virtually no significant energy observed by the hydrophone. This noise, hereinafter referred to as “V(z) noise,” often is so severe that without adequate tools for removal, the advantages of dual-component ocean bottom acquisition are practically nullified. V(z) noise occurs in both cable data (see Gaiser, J, “Vector-fidelity benefits of buried OBC detectors at Teal South,” 74th Annual Meeting, SEG, pp. 913-916, 2004) and node-type data (see Ray, A., B. Nolte, and D. Herron, “First nodal OBC acquisition from the Thunder Horse Field in the deep water of the Gulf of Mexico,” 74th Annual International Meeting, SEG, pp. 406-409, 2005). Often large in amplitude, V(z) noise can degrade P-Z summation and differencing and subsequent imaging, thus stimulating techniques for its removal (see Shatilo, A. P., R. E. Duren, and T. Rape, “Effect of noise suppression on quality of 2C OBC image,” 74th Annual International Meeting, SEG, Extended Abstracts, pp. 917-920, 2004, hereinafter referred to as Shatilo et al.). Observed characteristics of the noise include exhibiting converted wave moveout, being very weak or absent on the hydrophone, and being coherent on a receiver record, but random on a shot record (i.e., is not repeatable on closely spaced geophones).
The non-velocity filtering methods seek to exploit the fact that the hydrophone signal is not affected by anything like the V(z) noise. However, since up-going and down-going waves have different polarity relationships on hydrophone and geophone, previous methods have used water layer reverberation models to separate signal from noise (see Dragoset, B, “Geophysical applications of adaptive-noise cancellation,” 65th Annual International Meeting, SEG, Expanded Abstracts, pp. 1389-1392, 1995 and Brittan, J., and J. Starr, “Applications of adaptive noise attenuation to dual sensor seismic data,” 73rd Annual International Meeting, SEG, Expanded Abstracts, pp. 653-656, 2003). Use of a depth dependent modeled multiple period in any V(z) noise elimination technique introduces a 1-D approximation which makes it inappropriate in complex water bottom and subsurface environments and progressively incorrect as a function of increasing angle arrival of energy at the phone.
Shatilo et al. (2004) give an overview of previous attempts to solve the V(z) noise problem. They are quite successful with F-K based velocity filtering in the common receiver domain. This technique makes the often valid assumption that complete dip separation of primary and noise energy can be achieved on a receiver gather record through application of normal moveout corrections. With significant geologic complexity, both p-wave signal and the noise exciting shear wave will appear at a broad range of dip, necessitating a different decomposition approach. Additionally, F-K velocity filter will often suffer from spatial aliasing.
Accordingly, what is needed is an improved method of processing dual sensor data such that meaningful geophone data may be extracted.